Abstract
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on daytime benchmarks but struggle in nighttime scenarios due to limited visibility and challenging lighting conditions. To address these challenges, we propose \textbf{LIAR}, a novel framework that learns illumination-affined representations. LIAR first introduces Selective Low-light Image Enhancement (SLLIE), which leverages the illumination priors from daytime scenes to adaptively determine whether a nighttime image is genuinely dark or sufficiently well-lit, enabling more targeted global enhancement. Building on the illumination maps generated by SLLIE, LIAR further incorporates two illumination-aware components: 2D Illumination-guided Sampling (2D-IGS) and 3D Illumination-driven Projection (3D-IDP), to respectively tackle local underexposure and overexposure. Specifically, 2D-IGS modulates feature sampling positions according to illumination maps, assigning larger offsets to darker regions and smaller ones to brighter regions, thereby alleviating feature degradation in underexposed areas. Subsequently, 3D-IDP enhances semantic understanding in overexposed regions by constructing illumination intensity fields and supplying refined residual queries to the BEV context refinement process. Extensive experiments on both real and synthetic datasets demonstrate the superior performance of LIAR under challenging nighttime scenarios. The source code and pretrained models are available \href{this https URL}{here}.
Abstract (translated)
占用预测旨在估算被占据区域的3D空间分布及其相应的语义标签。现有的基于视觉的方法在白天基准测试中表现出色，但在夜间场景中由于能见度低和照明条件恶劣而面临挑战。为了解决这些问题，我们提出了**LIAR**（光照适应表示学习框架），该框架通过利用白天空间场景中的光照先验来改进夜间图像的处理。 具体来说，LIAR首先引入了选择性弱光图像增强(SLLIE)，它可以根据白天场景的光照先验自适应地判断夜间图像是否真正处于黑暗状态或照明充足，并进行针对性的整体提升。基于SLLIE生成的光照图，LIAR进一步整合了两个光照感知组件：2D光照引导采样(2D-IGS)和3D光照驱动投影(3D-IDP)，以分别解决局部曝光不足与过度曝光的问题。 具体而言，2D-IGS根据光照地图调节特征抽取的位置，在较暗的区域赋予较大的偏移值而在明亮区域给予较小的偏移值，从而缓解在曝光不足区域中的特征退化。随后，3D-IDP通过构建光照强度场和为BEV上下文细化过程提供精炼残差查询来提升过度曝光区域中的语义理解。 一系列在真实和合成数据集上的广泛实验表明，在具有挑战性的夜间场景中，LIAR展现出了卓越的性能。该框架的源代码和预训练模型可以在这里获取（原文链接）。
URL
https://arxiv.org/abs/2505.20641
